Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	133,495
2	South Dakota	131,092
3	Rhode Island	126,528
4	Utah	119,087
5	Tennessee	115,279
6	Arizona	114,956
7	Oklahoma	110,046
8	Iowa	109,733
9	Arkansas	108,922
10	Wisconsin	108,201
11	Nebraska	107,073
12	South Carolina	105,308
13	Alabama	104,376
14	Kansas	104,206
15	Mississippi	101,786
16	Indiana	101,259
17	Idaho	99,564
18	Nevada	97,843
19	New Jersey	97,840
20	Illinois	96,797
21	Montana	96,584
22	Wyoming	96,292
23	Georgia	96,190
24	Texas	95,129
25	Kentucky	95,064
26	Delaware	94,919
27	Louisiana	94,877
28	Missouri	94,343
29	Florida	93,641
30	California	92,263
31	New York	91,907
32	New Mexico	90,561
33	Minnesota	89,800
34	Massachusetts	89,354
35	North Carolina	86,127
36	Ohio	85,652
37	Connecticut	84,051
38	Alaska	83,431
39	Colorado	78,670
40	Pennsylvania	77,614
41	West Virginia	77,059
42	Virginia	70,993
43	Michigan	69,771
44	Maryland	66,310
45	District of Columbia	61,470
46	New Hampshire	59,387
47	Washington	47,108
48	Puerto Rico	43,322
49	Oregon	38,361
50	Maine	36,186
51	Vermont	28,588
52	Hawaii	20,419

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	New Jersey	423
2	Rhode Island	314
3	Michigan	306
4	Connecticut	278
5	Delaware	271
6	New York	262
7	Massachusetts	260
8	Vermont	238
9	South Carolina	220
10	Pennsylvania	209
11	Minnesota	205
12	West Virginia	187
13	Florida	185
14	New Hampshire	184
15	South Dakota	171
16	District of Columbia	164
17	Alaska	159
18	Colorado	156
19	Kansas	155
20	Maryland	154
21	Tennessee	153
22	North Carolina	152
23	Virginia	147
24	Maine	141
25	Georgia	131
26	Illinois	119
27	Idaho	118
28	Nebraska	112
29	Ohio	110
30	Indiana	107
31	Utah	104
32	Montana	101
33	Iowa	96
34	Louisiana	95
35	Texas	95
36	Washington	91
37	Kentucky	90
38	Puerto Rico	88
39	Wyoming	85
40	New Mexico	82
41	Alabama	81
42	North Dakota	81
43	Oklahoma	80
44	Wisconsin	77
45	Arizona	76
46	Missouri	71
47	Nevada	71
48	Mississippi	68
49	California	59
50	Oregon	57
51	Hawaii	55
52	Arkansas	47

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,722
2	New York	2,516
3	Rhode Island	2,458
4	Massachusetts	2,451
5	Mississippi	2,337
6	Arizona	2,300
7	Connecticut	2,199
8	South Dakota	2,173
9	Louisiana	2,157
10	Alabama	2,128
11	North Dakota	1,957
12	Pennsylvania	1,940
13	Indiana	1,923
14	New Mexico	1,857
15	Illinois	1,844
16	Arkansas	1,837
17	Iowa	1,799
18	South Carolina	1,753
19	Tennessee	1,700
20	Georgia	1,694
21	Michigan	1,692
22	Nevada	1,679
23	Kansas	1,664
24	Texas	1,642
25	Delaware	1,572
26	Ohio	1,568
27	Florida	1,526
28	District of Columbia	1,487
29	California	1,461
30	West Virginia	1,457
31	Missouri	1,437
32	Maryland	1,353
33	Montana	1,323
34	Kentucky	1,308
35	Wisconsin	1,243
36	Minnesota	1,214
37	Oklahoma	1,210
38	Wyoming	1,197
39	Virginia	1,186
40	Nebraska	1,160
41	North Carolina	1,131
42	Idaho	1,090
43	Colorado	1,067
44	New Hampshire	895
45	Washington	687
46	Puerto Rico	656
47	Utah	643
48	Oregon	563
49	Maine	542
50	Alaska	404
51	Vermont	350
52	Hawaii	318

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Kentucky	7
2	Massachusetts	4
3	Delaware	3
4	Louisiana	3
5	South Carolina	3
6	Arizona	2
7	California	2
8	Georgia	2
9	Maryland	2
10	New Jersey	2
11	Rhode Island	2
12	Texas	2
13	West Virginia	2
14	Arkansas	1
15	District of Columbia	1
16	Florida	1
17	Illinois	1
18	Indiana	1
19	Michigan	1
20	Mississippi	1
21	Montana	1
22	Nevada	1
23	New Mexico	1
24	New York	1
25	North Carolina	1
26	South Dakota	1
27	Vermont	1
28	Virginia	1
29	Alabama	0
30	Alaska	0
31	Colorado	0
32	Connecticut	0
33	Hawaii	0
34	Idaho	0
35	Iowa	0
36	Kansas	0
37	Maine	0
38	Minnesota	0
39	Missouri	0
40	Nebraska	0
41	New Hampshire	0
42	North Dakota	0
43	Ohio	0
44	Oklahoma	0
45	Oregon	0
46	Pennsylvania	0
47	Puerto Rico	0
48	Tennessee	0
49	Utah	0
50	Washington	0
51	Wisconsin	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	350,437	1	99
Chattahoochee	Georgia	311,176	2	99
Bent	Colorado	266,093	3	99
Dewey	South Dakota	243,551	4	99
Lincoln	Arkansas	243,550	5	99
Davidson	Tennessee	134,187	210	93
Richland	South Carolina	103,938	1040	66
York	South Carolina	100,029	1219	61
Orange	California	83,399	1997	36
Pierce	Washington	45,410	2905	7

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	8,346	1	99
Jerauld	South Dakota	7,948	2	99
Galax city	Virginia	7,878	3	99
Foard	Texas	7,792	4	99
Emporia city	Virginia	7,669	5	99
Orange	California	1,451	1789	43
Davidson	Tennessee	1,268	2011	35
Richland	South Carolina	1,256	2036	35
York	South Carolina	1,239	2061	34
Pierce	Washington	660	2719	13

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.